How to Use Demand Forecasting in Shopify: Step-by-Step Tutorial

Predict your Shopify sales for the next 30-90 days with data-driven demand forecasting

Introduction: Why Demand Forecasting Matters for Shopify Stores

One of the most critical questions for any e-commerce business is: "What will our sales be in the next 30-90 days?" Whether you're managing inventory, planning marketing campaigns, or budgeting for growth, accurate demand forecasting can be the difference between stockouts and overstocking, between missed opportunities and optimized cash flow.

Demand forecasting uses historical sales data, seasonal patterns, and statistical algorithms to predict future sales volumes. For Shopify merchants, this means you can anticipate customer demand before it happens, allowing you to make proactive decisions rather than reactive ones.

In this tutorial, you'll learn how to implement demand forecasting for your Shopify store, interpret the results, and apply these insights to real business decisions. By the end, you'll have a clear understanding of what to expect in your next quarter and how to prepare for it.

Prerequisites and Data Requirements

Before you begin forecasting demand, you need to ensure your Shopify store has the right foundation. Demand forecasting is only as good as the data it's built on, so let's verify you have everything needed.

What You'll Need:

Checking Your Data Quality:

Log into your Shopify admin and navigate to Analytics > Reports > Sales over time. Look for these indicators:

Important Note: If your store is brand new (less than 90 days of data), demand forecasting may produce unreliable results. Consider using industry benchmarks or competitor analysis instead until you build sufficient history.

Step 1: Understand Demand Forecasting Fundamentals

Before diving into the technical implementation, it's essential to understand what demand forecasting actually measures and how it works for e-commerce businesses.

What Is Demand Forecasting?

Demand forecasting is a predictive analytics technique that estimates future customer demand using historical data patterns. Unlike simple trend lines, modern forecasting algorithms account for:

Common Forecasting Methods:

For Shopify stores, several statistical approaches are commonly used:

The MCP Analytics demand forecasting service automatically selects the best algorithm based on your data characteristics, so you don't need to be a data scientist to get accurate predictions.

Expected Outcome:

After this step, you should understand that forecasting provides a probabilistic prediction rather than a guarantee. You'll receive a range of possible outcomes (confidence intervals) along with a most-likely estimate.

Step 2: Verify Prerequisites and Data Requirements

Now that you understand the fundamentals, let's verify your store is ready for forecasting analysis.

Action Items:

2.1 Check Your Data Timespan

Navigate to your Shopify Admin and run this check:

  1. Go to Analytics > Reports
  2. Select Sales over time
  3. Set the date range to "Last 12 months"
  4. Count the number of months with order data

What to look for: You should see at least 3 months of data, ideally 6-12 months. More data means better predictions, especially for capturing seasonal patterns.

2.2 Export Your Order Data

To prepare for analysis, export your historical orders:

  1. In Shopify Admin, go to Orders
  2. Click Export in the top right
  3. Select "All orders" or specify your date range
  4. Choose "Plain CSV file" format
  5. Click Export orders

Your exported CSV will include columns like:

Name, Email, Financial Status, Paid at, Fulfillment Status, Fulfilled at,
Accepts Marketing, Currency, Subtotal, Shipping, Taxes, Total, Discount Code,
Discount Amount, Shipping Method, Created at, Lineitem quantity, Lineitem name,
Lineitem price, Lineitem compare at price, Lineitem sku, Lineitem requires shipping

2.3 Verify Data Completeness

Open your exported CSV and check:

Expected Outcome:

You should have a clean CSV file with at least 90 days of order history, ready for forecasting analysis. If your data has gaps or errors, you may need to clean it first or consult with your Shopify support team.

Step 3: Access Your Shopify Order Data

With your data exported and verified, you're ready to connect it to a forecasting tool. This step walks through accessing your data programmatically for automated, ongoing forecasting.

Option A: Using Shopify API (Recommended for Automation)

For ongoing forecasting that updates automatically, connect via Shopify's API:

3.1 Create a Custom App

  1. In Shopify Admin, go to Settings > Apps and sales channels
  2. Click Develop apps (you may need to enable custom app development first)
  3. Click Create an app
  4. Name it "Demand Forecasting" and set yourself as the app developer
  5. Click Create app

3.2 Configure API Scopes

Your forecasting app needs read access to orders:

  1. Click Configure Admin API scopes
  2. Under "Orders," check read_orders
  3. Click Save
  4. Click Install app

3.3 Retrieve API Credentials

After installation, you'll receive an Admin API access token. Store this securely—you'll need it to fetch order data.

# Example: Fetch orders using Shopify API (Python)
import requests
from datetime import datetime, timedelta

SHOP_URL = "your-store.myshopify.com"
ACCESS_TOKEN = "your-admin-api-token"

headers = {
    "X-Shopify-Access-Token": ACCESS_TOKEN,
    "Content-Type": "application/json"
}

# Fetch orders from last 180 days
start_date = (datetime.now() - timedelta(days=180)).isoformat()
url = f"https://{SHOP_URL}/admin/api/2024-01/orders.json"
params = {
    "status": "any",
    "created_at_min": start_date,
    "limit": 250
}

response = requests.get(url, headers=headers, params=params)
orders = response.json()["orders"]

print(f"Retrieved {len(orders)} orders")
# Output: Retrieved 250 orders

Option B: Using MCP Analytics (Recommended for Simplicity)

For a no-code solution, use the MCP Analytics Shopify Demand Forecasting tool:

  1. Navigate to the demand forecasting analysis page
  2. Upload your exported CSV file from Step 2
  3. Configure your forecast parameters (time horizon, confidence level)
  4. Click "Run Analysis"

Expected Outcome:

Your order data is now accessible for analysis. You should have either API credentials set up for automated forecasting or your data uploaded to an analysis platform.

Step 4: Run Demand Forecasting Analysis

Now comes the exciting part—generating your actual sales forecast. This step transforms your historical data into actionable predictions.

Configure Your Forecast Parameters

Before running the analysis, you'll need to specify:

Running the Analysis

If you're using code with your exported data:

# Example: Demand forecasting with Prophet (Facebook's time series library)
from prophet import Prophet
import pandas as pd

# Load your Shopify order data
df = pd.read_csv('shopify_orders.csv')

# Prepare data for forecasting
df['Created at'] = pd.to_datetime(df['Created at'])
daily_sales = df.groupby(df['Created at'].dt.date)['Total'].sum().reset_index()
daily_sales.columns = ['ds', 'y']  # Prophet requires 'ds' and 'y' columns

# Initialize and fit the model
model = Prophet(
    yearly_seasonality=True,
    weekly_seasonality=True,
    daily_seasonality=False,
    interval_width=0.90  # 90% confidence intervals
)
model.fit(daily_sales)

# Generate 90-day forecast
future_dates = model.make_future_dataframe(periods=90)
forecast = model.predict(future_dates)

# View predictions
print(forecast[['ds', 'yhat', 'yhat_lower', 'yhat_upper']].tail(30))
# Output shows predicted sales (yhat) with confidence bounds for next 30 days

The analysis typically completes in 30-60 seconds, depending on your data volume. During processing, the algorithm:

  1. Detects trend patterns in your historical sales
  2. Identifies seasonal cycles (weekly, monthly, yearly)
  3. Accounts for outliers and anomalies
  4. Generates probabilistic forecasts with confidence intervals

Expected Outcome:

You'll receive a forecast dataset containing:

For example, a 30-day forecast might predict $45,000 in total revenue (ranging from $38,000 to $52,000 with 90% confidence).

Step 5: Interpret Your Forecast Results

Raw predictions are just numbers—the real value comes from interpreting what they mean for your business. This step teaches you how to read and understand your forecast.

Understanding Confidence Intervals

Your forecast includes three key values for each future date:

Example Interpretation:

Date: 2024-02-15
Predicted Sales: $1,520
Lower Bound (90%): $1,180
Upper Bound (90%): $1,860

Interpretation: On February 15th, we expect sales around $1,520. There's a 90%
probability that actual sales will fall between $1,180 and $1,860.

Identifying Trends

Look at your forecast line over time:

Recognizing Seasonal Patterns

Your forecast will reflect recurring patterns:

For instance, if you sell fitness equipment, your forecast might show a spike in early January (New Year's resolutions) and a dip in late summer. Understanding these patterns helps you plan inventory, staffing, and marketing campaigns accordingly.

Comparing to Historical Performance

Validate your forecast by comparing predictions to known outcomes:

# Compare forecast accuracy on historical data
actual_sales = df[df['date'] >= '2024-01-01']['total_sales']
predicted_sales = forecast[forecast['ds'] >= '2024-01-01']['yhat']

# Calculate Mean Absolute Percentage Error (MAPE)
mape = (abs(actual_sales - predicted_sales) / actual_sales).mean() * 100
print(f"Forecast Accuracy: {100 - mape:.1f}%")
# Output: Forecast Accuracy: 87.3%

An accuracy above 80% is generally considered good for e-commerce demand forecasting. Similar to evaluating statistical significance in A/B testing, you want to ensure your predictions meet a meaningful threshold before making major business decisions based on them.

Expected Outcome:

You should be able to answer questions like:

Step 6: Validate Forecast Accuracy

Before basing business decisions on your forecast, you need to verify it's reliable. This step shows you how to validate and improve forecast accuracy.

Backtesting Your Forecast

Backtesting means creating forecasts using only past data, then comparing predictions to what actually happened:

# Backtest example: Predict last 30 days using data before that
cutoff_date = '2024-01-01'
train_data = daily_sales[daily_sales['ds'] < cutoff_date]
test_data = daily_sales[daily_sales['ds'] >= cutoff_date]

# Train model on historical data only
model_backtest = Prophet()
model_backtest.fit(train_data)

# Predict the "future" (which already happened)
backtest_forecast = model_backtest.predict(test_data)

# Compare predictions to actuals
comparison = pd.DataFrame({
    'date': test_data['ds'],
    'actual': test_data['y'].values,
    'predicted': backtest_forecast['yhat'].values,
    'error_pct': abs(test_data['y'].values - backtest_forecast['yhat'].values) / test_data['y'].values * 100
})

print(comparison.head(10))

Key Accuracy Metrics

Evaluate your forecast using these metrics:

When Forecasts Fail: Red Flags

Watch for these warning signs:

Improving Forecast Accuracy

If validation reveals issues, try these improvements:

  1. Add more historical data: Extend your dataset further back in time
  2. Incorporate external factors: Marketing spend, website traffic, seasonality indicators
  3. Segment by product: Forecast individual product lines separately for more precision
  4. Update regularly: Re-run forecasts weekly or monthly as new data arrives

Expected Outcome:

You'll have quantified confidence in your forecast accuracy. For example: "Our model predicts next month's sales within 15% accuracy, with 88% of actual values falling within the confidence intervals." This gives you the assurance needed to act on predictions.

Step 7: Apply Forecasts to Business Planning

Now that you have validated forecasts, let's turn predictions into action. This step shows how to use demand forecasting for practical business decisions.

Inventory Planning

Use forecasts to optimize stock levels and avoid costly stockouts or excess inventory:

Calculate Required Inventory

# Determine inventory needs based on forecast
forecast_30day = forecast[forecast['ds'] <= '2024-03-15'].tail(30)
predicted_units = forecast_30day['yhat'].sum() / avg_order_value

# Add safety stock for upper bound
safety_stock = (forecast_30day['yhat_upper'].sum() - forecast_30day['yhat'].sum()) / avg_order_value

recommended_inventory = predicted_units + safety_stock
print(f"Recommended inventory for next 30 days: {recommended_inventory:.0f} units")
# Output: Recommended inventory for next 30 days: 1,247 units

Cash Flow Planning

Predict revenue and expenses to maintain healthy cash flow:

Marketing Budget Allocation

Time campaigns around predicted demand patterns:

Staffing and Operations

Align your team size with predicted order volume:

Scenario Planning

Use confidence bounds for best/worst case planning:

Scenario: Next Quarter (90 days)

Conservative Plan (Lower Bound): $127,000 in revenue
- Minimum inventory: 850 units
- Core staff only: 3 FTE
- Reduced marketing: $5,000/month

Expected Plan (Point Estimate): $158,000 in revenue
- Recommended inventory: 1,100 units
- Standard staff: 4 FTE
- Standard marketing: $8,000/month

Aggressive Plan (Upper Bound): $189,000 in revenue
- Maximum inventory: 1,350 units
- Expanded staff: 5 FTE
- Increased marketing: $12,000/month

Expected Outcome:

You'll have actionable plans for inventory, staffing, marketing, and cash management based on data-driven predictions rather than guesswork. Your decisions are now aligned with anticipated demand, reducing waste and maximizing profitability.

Try Demand Forecasting for Your Shopify Store

Ready to predict your next 30-90 days of sales with confidence? The MCP Analytics Shopify Demand Forecasting tool automates everything you've learned in this tutorial—no coding required.

What You'll Get:

Get Your Sales Forecast Now →

Next Steps: Advanced Forecasting Techniques

Once you've mastered basic demand forecasting, consider these advanced approaches:

1. Product-Level Forecasting

Instead of forecasting total sales, predict demand for individual products or categories. This enables more precise inventory management and helps identify which items drive growth.

2. Multi-Variable Forecasting

Incorporate external factors like marketing spend, website traffic, email open rates, or competitor pricing. Advanced techniques like AI-first data analysis pipelines can automatically discover which variables improve forecast accuracy.

3. Probabilistic Inventory Optimization

Use forecast distributions (not just point estimates) to calculate optimal reorder points and safety stock levels, minimizing both stockout costs and carrying costs.

4. Real-Time Forecast Updates

Set up automated pipelines that re-run forecasts daily or weekly as new orders arrive, keeping predictions current and adapting to changing market conditions.

5. Ensemble Forecasting

Combine multiple forecasting methods (e.g., ARIMA + Prophet + machine learning) to create more robust predictions. This approach, similar to ensemble methods in survival analysis, often outperforms single-model approaches.

Continue Learning:

Troubleshooting: Common Issues and Solutions

Even with clean data and proper setup, you may encounter challenges with demand forecasting. Here are solutions to common problems:

Problem 1: Forecast Shows No Trend or Seasonality

Symptoms: Predictions are flat, just showing average historical sales with no patterns

Causes:

Solutions:

Problem 2: Wide Confidence Intervals (Low Precision)

Symptoms: The gap between upper and lower bounds is very large (e.g., $10K-$50K)

Causes:

Solutions:

# Reduce uncertainty by incorporating external variables
model = Prophet()
model.add_regressor('marketing_spend')  # Add marketing as a predictor
model.add_regressor('email_subscribers')  # Add audience size
model.fit(enhanced_data)

# Result: Tighter confidence intervals by explaining more variance

Problem 3: Forecast Accuracy Degrades Over Time

Symptoms: Near-term predictions are accurate, but 60-90 day forecasts are way off

Causes:

Solutions:

Problem 4: API Rate Limits When Fetching Shopify Data

Symptoms: Error 429 "Too Many Requests" when pulling order data

Causes:

Solutions:

# Implement rate limiting in your data fetch
import time

all_orders = []
page_info = None

while True:
    params = {"limit": 250}
    if page_info:
        params["page_info"] = page_info

    response = requests.get(url, headers=headers, params=params)

    if response.status_code == 429:
        # Rate limited - wait and retry
        retry_after = int(response.headers.get('Retry-After', 2))
        time.sleep(retry_after)
        continue

    orders = response.json()["orders"]
    all_orders.extend(orders)

    # Check for next page
    link_header = response.headers.get('Link')
    if not link_header or 'rel="next"' not in link_header:
        break

    # Extract page_info for next request
    page_info = extract_page_info(link_header)
    time.sleep(0.5)  # Stay under rate limit

Problem 5: Forecast Doesn't Account for Known Future Events

Symptoms: You're planning a Black Friday sale, but forecast doesn't show expected spike

Causes:

Solutions:

# Add known holiday events to improve forecast
from prophet import Prophet

model = Prophet()

# Define holiday periods
holidays = pd.DataFrame({
    'holiday': 'black_friday',
    'ds': pd.to_datetime(['2023-11-24', '2024-11-29']),
    'lower_window': 0,
    'upper_window': 3  # 3-day Black Friday weekend effect
})

model = Prophet(holidays=holidays)
model.fit(daily_sales)

# Forecast now includes expected Black Friday spike

Problem 6: CSV Export Missing Required Columns

Symptoms: Exported Shopify data lacks date or revenue fields needed for forecasting

Causes:

Solutions:

Getting Additional Help

If you encounter issues not covered here: